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ACADEMIC ENDEAVORS

Diabetes Analysis Dashboard

Aug 2023

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• Analyzed over 10,000 diagnostic lab data points to identify key performance indicators (KPIs) impacting profitability at the store, test, and result levels, resulting in targeted business strategies with a projected revenue increase of 15%.
• Utilized Power BI to synthesize insights and trends from data into 5 strategic initiatives and recommendations, presenting a multi-level analysis to stakeholders and facilitating informed decisions.
• Performed data cleaning, and data visualization to create a clear, concise, and captivating visualization presentation.

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• Designed and implemented a Power BI dashboard to assess sales efficiency across 4 geographical regions and multiple product categories, enabling a holistic view of organizational performance, and serving as a pivotal tool for executive decision-making

• Incorporated interactive features such as slicers and advanced tooltips to provide real-time, user-specific insights into sales and profit correlations, product category efficiencies, and forecasting, thereby enhancing usability

• Integrated a scatterplot visual to identify sales-profit correlations for each region, product category, and product sub-category, to drive targeted cost-optimization strategies, and enhance the dashboard's analytical depth in assessing efficiency

Sales Efficiency Dashboard

Aug 2023

Agile Scrum Master Simulation

Arizona State University, Tempe, AZ

Jun 2023

  • Facilitated Scrum events, from sprint planning to sprint retrospective, guiding the team in self-organization & cross-functionality, delivering a minimum viable product to enhance employee benefits and payroll experience

  • Achieved all planned 18 minimum viable product stories in 4 sprints, promoting a collaborative and productive environment

  • Evaluated milestones and deliverable performance for each sprint through burndown charts and velocity trend graphs

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  • Researched food recipe trends for Instacart, narrowing down the focus by identifying user pain points, optimizing application, and increasing KPIs such as customer satisfaction, and profitability

  • Applied Latent Dirichlet Allocation algorithm (LDA) & Sentiment analysis to a large unstructured dataset collected from Reddit

  • Performed a full analytical pipeline, leveraging digital social media, and using agile methodology to gain a market understanding

Recipe Trends Using Social Media Analytics

Arizona State University, Tempe, AZ

Apr 2023

Brewery Expansion Visualization

Arizona State University, Tempe, AZ

Mar 2023

  • Recommended expansion areas considering company strengths, volume of breweries, and offering growth opportunities in 2 states

  • Explored complex dataset of 1000+ data entries across 5+ sheets in Excel containing a dataset, recognizing data patterns and insights

  • Narrated a story presenting 6+ visualizations on Power BI and Tableau, keeping in mind pre-attentive attributes and quality of the deliverable while incorporating feedback

  • Mitigated risks and evaluated tradeoffs by solving 7 key challenges to establish a secure information environment

  • Gauged practices such as extending deep security to cloud or prioritizing training to make decisions, minimizing risk, and saving the company from crypto-ransomware attack

  • Collaborated with team in assessing risk management process, and was the go-to person to build a well-defined report using BLUF

Hospital Crisis Management Simulation

Arizona State University, Tempe, AZ

Mar 2023

IT Firm Crisis Management Simulation

Arizona State University, Tempe, AZ

Feb 2023

  • Analyzed risk-mitigating and various pricing options such as investing in breach detection technology, for company’s app launch

  • Evaluated options to minimize risk, and support the company by allocating budget wisely

  • Successfully saved millions by preventing data from getting compromised and keeping firm’s reputation & operations intact

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  • Developed an actionable plan to predict customer default on credit cards, establishing machine learning models - Random Forest & AdaBoost, achieving an accuracy of 86.5%

  • Applied feature engineering and data analytics targeting important features from a 26,000 dataset to increase profits by 35%

  • Aligned business goals & priorities, proposing changes with a long-term vision of impacting areas such as direct marketing, manufacturing

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Credit Card Default Prediction System

Arizona State University, Tempe, AZ

Feb 2023

Climate Change and Fast Fashion Analysis

Arizona State University, Tempe, AZ

Dec 2022

  • Implemented compelling data visualizations on Tableau, demonstrating the impact of fast fashion products on climate change and identifying 3+ areas for improvement

  • Conducted exploratory data analysis to provide strategic and innovative inputs creating an effective storyline to simplify complexity

  • Brainstormed techniques to reduce cognitive burden, enhancing team effort and performance while maintaining high velocity of work

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  • Developed a machine learning model to predict credit card approval using two-class logistic regression and the two-class boosted decision tree algorithm, achieving an accuracy of 89% and 97% respectively

  • Deployed tools on Microsoft Azure to train and optimize models

  • Analyzed and experimented with 12+ parameters to improve model performance

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Credit Card Approval Prediction System

Arizona State University, Tempe, AZ

Dec 2022

Traffic Sign Detection and Recognition System​

Arizona State University, Tempe, AZ

May 2020

  • Designed a real-time model using SVM algorithm and image processing techniques including thresholding and histogram equalization, to detect and recognize traffic signs based on shapes, with an 82.5% accuracy

  • Complied Python scripts on Jupyter Notebook to formulate a learning model, and generated a thorough report showing domain relevance, resources, timelines, and summary

  • Presented recommendations and scope for implementation for automobile companies

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  • Devised a prediction system using Python to predict house prices for Mumbai city, covering 30+ regions

  • Conducted research to identify 6+ factors that influence house prices such as the area, number of bedrooms, and additional facilities

  • Applied the Multivariate linear regression technique and attained an accuracy of 87%

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House Price Prediction System​

Arizona State University, Tempe, AZ

May 2019

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